import random
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case = False						):
					_UpperCamelCase			     =     {i: [] for i in range(__snake_case						)}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(__snake_case						)
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(__snake_case						):
										for j in range(i + 1  ,     __snake_case						):
															if random.random() < probability:
																				graph[i].append(__snake_case						)
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(__snake_case						)
					return graph
def 					_snake_case	(  __snake_case						):
					return {
					    i: [j for j in range(__snake_case						) if i != j] for i in range(__snake_case						)
					}


if __name__ == "__main__":
					import doctest

					doctest.testmod()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a      :  Dict			     =			{
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Any			     =			['LxmertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			[
	    'LxmertEncoder',
	    'LxmertForPreTraining',
	    'LxmertForQuestionAnswering',
	    'LxmertModel',
	    'LxmertPreTrainedModel',
	    'LxmertVisualFeatureEncoder',
	    'LxmertXLayer',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			[
	    'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFLxmertForPreTraining',
	    'TFLxmertMainLayer',
	    'TFLxmertModel',
	    'TFLxmertPreTrainedModel',
	    'TFLxmertVisualFeatureEncoder',
	]


if TYPE_CHECKING:
	from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
	from .tokenization_lxmert import LxmertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_lxmert_fast import LxmertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lxmert import (
		    LxmertEncoder,
		    LxmertForPreTraining,
		    LxmertForQuestionAnswering,
		    LxmertModel,
		    LxmertPreTrainedModel,
		    LxmertVisualFeatureEncoder,
		    LxmertXLayer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_lxmert import (
		    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLxmertForPreTraining,
		    TFLxmertMainLayer,
		    TFLxmertModel,
		    TFLxmertPreTrainedModel,
		    TFLxmertVisualFeatureEncoder,
		)

else:
	import sys

	a      :  str			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






